icelake march

Tests for a future article. Intel Core i7-1065G7 testing with a Dell 06CDVY (1.0.9 BIOS) and Intel Iris Plus ICL GT2 16GB on Ubuntu 23.10 via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2403278-NE-ICELAKEMA14
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Timed Code Compilation 2 Tests
C/C++ Compiler Tests 2 Tests
CPU Massive 9 Tests
Creator Workloads 9 Tests
Encoding 2 Tests
Game Development 2 Tests
HPC - High Performance Computing 4 Tests
Imaging 2 Tests
Machine Learning 4 Tests
Multi-Core 10 Tests
NVIDIA GPU Compute 2 Tests
Intel oneAPI 2 Tests
Programmer / Developer System Benchmarks 2 Tests
Python Tests 4 Tests
Renderers 2 Tests
Server CPU Tests 5 Tests

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March 26
  4 Hours, 59 Minutes
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March 27
  4 Hours, 48 Minutes
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icelake march Suite 1.0.0 System Test suite extracted from icelake march. pts/blender-4.1.0 -b ../pavillon_barcelone_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Pabellon Barcelona - Compute: CPU-Only pts/tensorflow-2.2.0 --device cpu --batch_size=64 --model=resnet50 Device: CPU - Batch Size: 64 - Model: ResNet-50 pts/blender-4.1.0 -b ../junkshop.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Junkshop - Compute: CPU-Only pts/blender-4.1.0 -b ../fishy_cat_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: Fishy Cat - Compute: CPU-Only pts/pytorch-1.1.0 cpu 32 efficientnet_v2_l Device: CPU - Batch Size: 32 - Model: Efficientnet_v2_l pts/pytorch-1.1.0 cpu 16 efficientnet_v2_l Device: CPU - Batch Size: 16 - Model: Efficientnet_v2_l pts/pytorch-1.1.0 cpu 64 efficientnet_v2_l Device: CPU - Batch Size: 64 - Model: Efficientnet_v2_l pts/blender-4.1.0 -b ../bmw27_gpu.blend -o output.test -x 1 -F JPEG -f 1 -- --cycles-device CPU Blend File: BMW27 - Compute: CPU-Only pts/pytorch-1.1.0 cpu 32 resnet152 Device: CPU - Batch Size: 32 - Model: ResNet-152 pts/build-linux-kernel-1.16.0 defconfig Build: defconfig pts/pytorch-1.1.0 cpu 64 resnet152 Device: CPU - Batch Size: 64 - Model: ResNet-152 pts/pytorch-1.1.0 cpu 16 resnet152 Device: CPU - Batch Size: 16 - Model: ResNet-152 pts/tensorflow-2.2.0 --device cpu --batch_size=32 --model=resnet50 Device: CPU - Batch Size: 32 - Model: ResNet-50 pts/stockfish-1.5.0 Chess Benchmark pts/tensorflow-2.2.0 --device cpu --batch_size=64 --model=googlenet Device: CPU - Batch Size: 64 - Model: GoogLeNet pts/pytorch-1.1.0 cpu 1 efficientnet_v2_l Device: CPU - Batch Size: 1 - Model: Efficientnet_v2_l pts/tensorflow-2.2.0 --device cpu --batch_size=16 --model=resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/jpegxl-1.6.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 80 --num_reps 80 Input: JPEG - Quality: 80 pts/jpegxl-1.6.0 sample-4.png out.jxl -q 80 --num_reps 80 Input: PNG - Quality: 80 pts/pytorch-1.1.0 cpu 64 resnet50 Device: CPU - Batch Size: 64 - Model: ResNet-50 pts/pytorch-1.1.0 cpu 32 resnet50 Device: CPU - Batch Size: 32 - Model: ResNet-50 pts/pytorch-1.1.0 cpu 16 resnet50 Device: CPU - Batch Size: 16 - Model: ResNet-50 pts/svt-av1-2.12.0 --preset 4 -n 160 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 4 - Input: Bosphorus 4K pts/tensorflow-2.2.0 --device cpu --batch_size=32 --model=googlenet Device: CPU - Batch Size: 32 - Model: GoogLeNet pts/jpegxl-1.6.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 100 --num_reps 20 Input: JPEG - Quality: 100 pts/jpegxl-1.6.0 sample-4.png out.jxl -q 100 --num_reps 20 Input: PNG - Quality: 100 pts/pytorch-1.1.0 cpu 1 resnet152 Device: CPU - Batch Size: 1 - Model: ResNet-152 pts/jpegxl-1.6.0 sample-4.png out.jxl -q 90 --num_reps 50 Input: PNG - Quality: 90 pts/jpegxl-1.6.0 --lossless_jpeg=0 sample-photo-6000x4000.JPG out.jxl -q 90 --num_reps 50 Input: JPEG - Quality: 90 pts/tensorflow-2.2.0 --device cpu --batch_size=64 --model=alexnet Device: CPU - Batch Size: 64 - Model: AlexNet pts/build-mesa-1.1.0 Time To Compile pts/onednn-3.4.0 --rnn --batch=inputs/rnn/perf_rnn_training --engine=cpu Harness: Recurrent Neural Network Training - Engine: CPU pts/onednn-3.4.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --engine=cpu Harness: Recurrent Neural Network Inference - Engine: CPU pts/primesieve-1.10.0 1e12 Length: 1e12 pts/tensorflow-2.2.0 --device cpu --batch_size=16 --model=googlenet Device: CPU - Batch Size: 16 - Model: GoogLeNet pts/svt-av1-2.12.0 --preset 8 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 8 - Input: Bosphorus 4K pts/tensorflow-2.2.0 --device cpu --batch_size=32 --model=alexnet Device: CPU - Batch Size: 32 - Model: AlexNet pts/v-ray-1.5.0 -m vray Mode: CPU pts/openvino-1.5.0 -m models/intel/face-detection-0206/FP16/face-detection-0206.xml -d CPU Model: Face Detection FP16 - Device: CPU pts/openvino-1.5.0 -m models/intel/face-detection-0206/FP16-INT8/face-detection-0206.xml -d CPU Model: Face Detection FP16-INT8 - Device: CPU pts/pytorch-1.1.0 cpu 1 resnet50 Device: CPU - Batch Size: 1 - Model: ResNet-50 pts/openvino-1.5.0 -m models/intel/person-detection-0303/FP32/person-detection-0303.xml -d CPU Model: Person Detection FP32 - Device: CPU pts/openvino-1.5.0 -m models/intel/machine-translation-nar-en-de-0002/FP16/machine-translation-nar-en-de-0002.xml -d CPU Model: Machine Translation EN To DE FP16 - Device: CPU pts/openvino-1.5.0 -m models/intel/person-detection-0303/FP16/person-detection-0303.xml -d CPU Model: Person Detection FP16 - Device: CPU pts/rocksdb-1.6.0 --benchmarks="fillsync" Test: Random Fill Sync pts/openvino-1.5.0 -m models/intel/road-segmentation-adas-0001/FP16-INT8/road-segmentation-adas-0001.xml -d CPU Model: Road Segmentation ADAS FP16-INT8 - Device: CPU pts/openvino-1.5.0 -m models/intel/person-vehicle-bike-detection-2004/FP16/person-vehicle-bike-detection-2004.xml -d CPU Model: Person Vehicle Bike Detection FP16 - Device: CPU pts/openvino-1.5.0 -m models/intel/noise-suppression-poconetlike-0001/FP16/noise-suppression-poconetlike-0001.xml -d CPU Model: Noise Suppression Poconet-Like FP16 - Device: CPU pts/openvino-1.5.0 -m models/intel/road-segmentation-adas-0001/FP16/road-segmentation-adas-0001.xml -d CPU Model: Road Segmentation ADAS FP16 - Device: CPU pts/openvino-1.5.0 -m models/intel/person-reidentification-retail-0277/FP16/person-reidentification-retail-0277.xml -d CPU Model: Person Re-Identification Retail FP16 - Device: CPU pts/openvino-1.5.0 -m models/intel/handwritten-english-recognition-0001/FP16-INT8/handwritten-english-recognition-0001.xml -d CPU Model: Handwritten English Recognition FP16-INT8 - Device: CPU pts/openvino-1.5.0 -m models/intel/vehicle-detection-0202/FP16-INT8/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16-INT8 - Device: CPU pts/rocksdb-1.6.0 --benchmarks="fillrandom" Test: Random Fill pts/openvino-1.5.0 -m models/intel/handwritten-english-recognition-0001/FP16/handwritten-english-recognition-0001.xml -d CPU Model: Handwritten English Recognition FP16 - Device: CPU pts/openvino-1.5.0 -m models/intel/face-detection-retail-0005/FP16-INT8/face-detection-retail-0005.xml -d CPU Model: Face Detection Retail FP16-INT8 - Device: CPU pts/openvino-1.5.0 -m models/intel/vehicle-detection-0202/FP16/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16 - Device: CPU pts/openvino-1.5.0 -m models/intel/weld-porosity-detection-0001/FP16/weld-porosity-detection-0001.xml -d CPU Model: Weld Porosity Detection FP16 - Device: CPU pts/openvino-1.5.0 -m models/intel/face-detection-retail-0005/FP16/face-detection-retail-0005.xml -d CPU Model: Face Detection Retail FP16 - Device: CPU pts/openvino-1.5.0 -m models/intel/weld-porosity-detection-0001/FP16-INT8/weld-porosity-detection-0001.xml -d CPU Model: Weld Porosity Detection FP16-INT8 - Device: CPU pts/rocksdb-1.6.0 --benchmarks="overwrite" Test: Overwrite pts/openvino-1.5.0 -m models/intel/age-gender-recognition-retail-0013/FP16-INT8/age-gender-recognition-retail-0013.xml -d CPU Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU pts/openvino-1.5.0 -m models/intel/age-gender-recognition-retail-0013/FP16/age-gender-recognition-retail-0013.xml -d CPU Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU pts/rocksdb-1.6.0 --benchmarks="readwhilewriting" Test: Read While Writing pts/rocksdb-1.6.0 --benchmarks="updaterandom" Test: Update Random pts/rocksdb-1.6.0 --benchmarks="readrandomwriterandom" Test: Read Random Write Random pts/rocksdb-1.6.0 --benchmarks="readrandom" Test: Random Read pts/jpegxl-decode-1.6.0 --num_reps=250 CPU Threads: All pts/tensorflow-2.2.0 --device cpu --batch_size=16 --model=alexnet Device: CPU - Batch Size: 16 - Model: AlexNet pts/jpegxl-decode-1.6.0 --num_threads=1 --num_reps=90 CPU Threads: 1 pts/svt-av1-2.12.0 --preset 4 -n 160 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 4 - Input: Bosphorus 1080p pts/compress-pbzip2-1.6.1 FreeBSD-13.0-RELEASE-amd64-memstick.img Compression pts/svt-av1-2.12.0 --preset 12 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 12 - Input: Bosphorus 4K pts/svt-av1-2.12.0 --preset 13 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 13 - Input: Bosphorus 4K pts/tensorflow-2.2.0 --device cpu --batch_size=1 --model=resnet50 Device: CPU - Batch Size: 1 - Model: ResNet-50 pts/onednn-3.4.0 --deconv --batch=inputs/deconv/shapes_1d --engine=cpu Harness: Deconvolution Batch shapes_1d - Engine: CPU pts/svt-av1-2.12.0 --preset 8 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 8 - Input: Bosphorus 1080p pts/encode-wavpack-1.5.0 WAV To WavPack pts/onednn-3.4.0 --ip --batch=inputs/ip/shapes_1d --engine=cpu Harness: IP Shapes 1D - Engine: CPU pts/tensorflow-2.2.0 --device cpu --batch_size=1 --model=alexnet Device: CPU - Batch Size: 1 - Model: AlexNet pts/draco-1.6.1 -i church.ply Model: Church Facade pts/onednn-3.4.0 --ip --batch=inputs/ip/shapes_3d --engine=cpu Harness: IP Shapes 3D - Engine: CPU pts/rocksdb-1.6.0 --benchmarks="fillseq" Test: Sequential Fill pts/tensorflow-2.2.0 --device cpu --batch_size=1 --model=googlenet Device: CPU - Batch Size: 1 - Model: GoogLeNet pts/draco-1.6.1 -i lion.ply Model: Lion pts/onednn-3.4.0 --conv --batch=inputs/conv/shapes_auto --engine=cpu Harness: Convolution Batch Shapes Auto - Engine: CPU pts/svt-av1-2.12.0 --preset 12 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 12 - Input: Bosphorus 1080p pts/onednn-3.4.0 --deconv --batch=inputs/deconv/shapes_3d --engine=cpu Harness: Deconvolution Batch shapes_3d - Engine: CPU pts/svt-av1-2.12.0 --preset 13 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 13 - Input: Bosphorus 1080p